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AN APPROACH USING AFFECTIVE COMPUTING TO PREDICT INTERACTION QUALITY FROM CONVERSATIONS
Title
AN APPROACH USING AFFECTIVE COMPUTING TO PREDICT INTERACTION QUALITY FROM CONVERSATIONS.
Creator
Matic, Richard N., Maniaci, Michael, Florida Atlantic University, Department of Psychology, Charles E. Schmidt College of Science
Abstract/Description
John Gottman’s mathematical models have been shown to accurately predict a couple’s style of interaction using only the sentiments found in the couple’s conversations. I derived speaker sentiment slopes from 151 recorded dyadic audio conversations from the IEMOCAP dataset through an IBM Watson emotion recognition pipeline and assessed its accuracy as input for a Gottman model by comparing the cumulative speaker sentiment slope for each conversation produced from predicted emotion codes to...
Show moreJohn Gottman’s mathematical models have been shown to accurately predict a couple’s style of interaction using only the sentiments found in the couple’s conversations. I derived speaker sentiment slopes from 151 recorded dyadic audio conversations from the IEMOCAP dataset through an IBM Watson emotion recognition pipeline and assessed its accuracy as input for a Gottman model by comparing the cumulative speaker sentiment slope for each conversation produced from predicted emotion codes to that produced from groundtruth codes provided by IEMOCAP. Watson produced sentiment slopes strongly correlated with those produced by groundtruth emotion codes. An abbreviated pipeline was also assessed consisting just of the Watson textual emotion recognition model using IEMOCAP’s human transcriptions as input. It produced predicted sentiment slopes very strongly correlated with those produced by groundtruth. The research demonstrated that artificial intelligence has potential to be used to predict interaction quality from short samples of conversational data.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00014023
Subject Headings
Affective Computing, Emotion recognition, Artificial intelligence
Format
Document (PDF)
INCORPORATING EMOTION RECOGNITION IN CO-ADAPTIVE SYSTEMS
Title
INCORPORATING EMOTION RECOGNITION IN CO-ADAPTIVE SYSTEMS.
Creator
Al-Omair, Osamah M., Huang, Shihong, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
The collaboration between human and computer systems has grown astronomically over the past few years. The ability of software systems adapting to human's input is critical in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. However, it is not always clear what kinds of human’s input should be considered to enhance the effectiveness of human and system co-adaptation. To address this issue,...
Show moreThe collaboration between human and computer systems has grown astronomically over the past few years. The ability of software systems adapting to human's input is critical in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. However, it is not always clear what kinds of human’s input should be considered to enhance the effectiveness of human and system co-adaptation. To address this issue, this research describes an approach that focuses on incorporating human emotion to improve human-computer co-adaption. The key idea is to provide a formal framework that incorporates human emotions as a foundation for explainability into co-adaptive systems, especially, how software systems recognize human emotions and adapt the system’s behaviors accordingly. Detecting and recognizing optimum human emotion is a first step towards human and computer symbiosis. As the first step of this research, we conduct a comparative review for a number of technologies and methods for emotion recognition. Specifically, testing the detection accuracy of facial expression recognition of different cloud-services, algorithms, and methods. Secondly, we study the application of emotion recognition within the areas of e-learning, robotics, and explainable artificial intelligence (XAI). We propose a formal framework that incorporates human emotions into an adaptive e-learning system, to create a more personalized learning experience for higher quality of learning outcomes. In addition, we propose a framework for a co-adaptive Emotional Support Robot. This human-centric framework adopts a reinforced learning approach where the system assesses its own emotional re-actions.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013926
Subject Headings
Emotion recognition, Human-computer interaction, Affective Computing
Format
Document (PDF)
A PROBABILISTIC CHECKING MODEL FOR EFFECTIVE EXPLAINABILITY BASED ON PERSONALITY TRAITS
Title
A PROBABILISTIC CHECKING MODEL FOR EFFECTIVE EXPLAINABILITY BASED ON PERSONALITY TRAITS.
Creator
Alharbi, Mohammed N., Huang, Shihong, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
It is becoming increasingly important for an autonomous system to be able to explain its actions to humans in order to improve trust and enhance human-machine collaboration. However, providing the most appropriate kind of explanations – in terms of length, format, and presentation mode of explanations at the proper time – is critical to enhancing their effectiveness. Explanation entails costs, such as the time it takes to explain and for humans to comprehend and respond. Therefore, the actual...
Show moreIt is becoming increasingly important for an autonomous system to be able to explain its actions to humans in order to improve trust and enhance human-machine collaboration. However, providing the most appropriate kind of explanations – in terms of length, format, and presentation mode of explanations at the proper time – is critical to enhancing their effectiveness. Explanation entails costs, such as the time it takes to explain and for humans to comprehend and respond. Therefore, the actual improvement in human-system tasks from explanations (if any) is not always obvious, particularly given various forms of uncertainty in knowledge about humans. In this research, we propose an approach to address this issue. The key idea is to provide a structured framework that allows a system to model and reason about human personality traits as critical elements to guide proper explanation in human and system collaboration. In particular, we focus on the two concerns of modality and amount of explanation in order to optimize the explanation experience and improve overall system-human utility. Our models are based on probabilistic modeling and analysis (PRISM-games) to determine at run time what the most effective explanation under uncertainty is. To demonstrate our approach, we introduce a self-adaptative system called Grid – a virtual game – and the Stock Prediction Engine (SPE), which allows an automated system and a human to collaborate on the game and stock investments. Our evaluation of these exemplars, through simulation, demonstrates that a human subject’s performance and overall human-system utility is improved when considering the psychology of human personality traits in providing explanations.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013894
Subject Headings
Human-computer interaction, Probabilistic modelling, Human-machine systems, Affective Computing
Format
Document (PDF)